Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients

Troels Siggaard,Isabella F Jørgensen,Jessica X Hjaltelin,Alejandro Aguayo-Orozco,Amalie D Haue,Karina Banasik,Søren Brunak,Anders Boeck Jensen,Roc Reguant,Mette Lademann

doi:10.1038/s41467-020-18682-4

Abstract

We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories. Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For example, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph. Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV).

Highlights

We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register
All entries in National Patient Register (NPR) are time stamped and linked to the national identification number that uniquely indexes every resident in Denmark
Events are linked over time unproblematically, which this study benefitted from in computing mortality by linkage of patients from NPR to The Danish Register of Causes of Death[28]

Summary

Introduction

We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. The need to understand at the molecular level how etiological factors impact co-occurring and interacting diseases is growing, where strategies typically benefit from the application of network biology concepts[1,10,11] These efforts should match disease progression observations made in large-scale, population-wide health data as already demonstrated in uncovering associations between complex disease and Mendelian loci[2] and estimates of heritability in the absence of genetic data[4]. Techniques like whole-genome sequencing and clinical proteomics produce data in a disease spectrum-wide manner enabling analysis of global interactions between disease pathways for subsequent linkage to actionable mechanisms[15,16,17] Another contributing factor is the health economic incentive to study multimorbidity as medical management is complex and costly[18]. Such work is likely to lead to revisions in the disease nomenclature; for example by redefining diseases based on disease history data and genetic risk profiles as opposed to the present paradigm where conclusions are derived from single encounters with the health care system[24,25]

Methods

Results

Conclusion